A Study on Mammographic Image Modelling and Classification Using Multiple Databases

Abstract

Within computer aided mammography, there are many image analysis methods have been developed for mammographic image classification. Some of these were developed and validated using well known publicly available databases, and others may have chosen to use independent/private databases for their investigations. Often, despite the promising results described in the literature, it is not unusual to see when adapting an established method with the recommended configurations for a different database, the obtained results are not in line with expectation. This paper presents results of a study with respect to the implications of mammographic image classification using different classifiers trained with variations, such as differences in parameter settings, classifiers, using single databases, combined and across databases. The results indicated that it is unlikely to have an universal parameter settings and classifiers, which can be used to achieve the best classification without tuning. Additional databases used at the training stages do not necessarily lead to more accurate density classifications; whilst classifiers trained with images obtained using one type of image acquisition are not ideal for classifying images obtained using different image acquisition. The related issues of optimal parameter configuration, classifier selection, and utilising single or multiple databases at the training stage are discussed.